



'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							str             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					Optional[int]					):
							# initialize config
							if "resnet-50" in model_name:
														SCREAMING_SNAKE_CASE       :   List[str]         =      ResNetConfig.from_pretrained('''microsoft/resnet-50'''					)
							elif "resnet-101" in model_name:
														SCREAMING_SNAKE_CASE       :   Dict         =      ResNetConfig.from_pretrained('''microsoft/resnet-101'''					)
							else:
														raise ValueError('''Model name should include either resnet50 or resnet101'''					)

							SCREAMING_SNAKE_CASE       :   Any         =      DetrConfig(use_timm_backbone=A       ,		backbone_config=A					)

							# set label attributes
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''panoptic''' in model_name
							if is_panoptic:
														SCREAMING_SNAKE_CASE       :   Tuple         =      250
							else:
														SCREAMING_SNAKE_CASE       :   Dict         =      91
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''huggingface/label-files'''
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''coco-detection-id2label.json'''
														SCREAMING_SNAKE_CASE       :   Tuple         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {v: k for k, v in idalabel.items()}

							return config, is_panoptic
def        UpperCAmelCase (    A	:					str					):
							# here we list all keys to be renamed (original name on the left, our name on the right)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      []

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')					)
							rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')					)
							rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')					)
							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')					)
							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')					)
							# stages
							for stage_idx in range(len(config.backbone_config.depths					)					):
														for layer_idx in range(config.backbone_config.depths[stage_idx]					):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																												    )					)
																					# 3 convs
																					for i in range(3					):
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																												    )					)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																												    )					)
    # fmt: on

							for i in range(config.encoder_layers					):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
														        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
														    )					)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")					)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")					)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")					)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")					)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")					)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")					)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")					)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")					)
														rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")					)
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
														        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
														    )					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")					)
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
														        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
														    )					)
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
														        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
														    )					)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")					)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")					)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")					)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")					)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")					)
														rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")					)

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('''input_proj.weight''', '''input_projection.weight'''),
							        ('''input_proj.bias''', '''input_projection.bias'''),
							        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
							        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
							        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
							        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
							        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
							        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
							        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
							        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
							        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
							        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
							        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
							    ]					)

							return rename_keys
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					Optional[int]					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      state_dict.pop(A					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      val
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Optional[Any]=False					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ''''''
							if is_panoptic:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      '''detr.'''

							# first: transformer encoder
							for i in range(6					):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   Dict         =      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   List[str]         =      in_proj_weight[:256, :]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_bias[:256]
														SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_weight[256:512, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[256:512]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_weight[-256:, :]
														SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6					):
														# read in weights + bias of input projection layer of self-attention
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   Dict         =      state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_weight[:256, :]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_bias[:256]
														SCREAMING_SNAKE_CASE       :   List[str]         =      in_proj_weight[256:512, :]
														SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[256:512]
														SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_weight[-256:, :]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(
														    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight_cross_attn[:256, :]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_bias_cross_attn[:256]
														SCREAMING_SNAKE_CASE       :   int         =      in_proj_weight_cross_attn[256:512, :]
														SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_bias_cross_attn[256:512]
														SCREAMING_SNAKE_CASE       :   List[str]         =      in_proj_weight_cross_attn[-256:, :]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_bias_cross_attn[-256:]
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   List[str]         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      Image.open(requests.get(A       ,		stream=A					).raw					)

							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					List[str]=None       ,		A	:					List[str]=False					):
							SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      get_detr_config(A					)

							# load original model from torch hub
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
							    '''detr-resnet-50''': '''detr_resnet50''',
							    '''detr-resnet-101''': '''detr_resnet101''',
							}
							logger.info(F"""Converting model {model_name}..."""					)
							SCREAMING_SNAKE_CASE       :   Any         =      torch.hub.load('''facebookresearch/detr'''       ,		model_name_to_original_name[model_name]       ,		pretrained=A					).eval()
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(A					):
														if is_panoptic:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''detr.''' + src
														rename_key(A       ,		A       ,		A					)
							# query, key and value matrices need special treatment
							read_in_q_k_v(A       ,		is_panoptic=A					)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''detr.model.''' if is_panoptic else '''model.'''
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('''detr'''					)
																					    and not key.startswith('''class_labels_classifier'''					)
																					    and not key.startswith('''bbox_predictor'''					)
																					):
																												SCREAMING_SNAKE_CASE       :   Dict         =      state_dict.pop(A					)
																												SCREAMING_SNAKE_CASE       :   Any         =      val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												SCREAMING_SNAKE_CASE       :   Dict         =      state_dict.pop(A					)
																												SCREAMING_SNAKE_CASE       :   Any         =      val
																					elif key.startswith('''bbox_attention'''					) or key.startswith('''mask_head'''					):
																												continue
																					else:
																												SCREAMING_SNAKE_CASE       :   str         =      state_dict.pop(A					)
																												SCREAMING_SNAKE_CASE       :   Dict         =      val
														else:
																					if not key.startswith('''class_labels_classifier'''					) and not key.startswith('''bbox_predictor'''					):
																												SCREAMING_SNAKE_CASE       :   Any         =      state_dict.pop(A					)
																												SCREAMING_SNAKE_CASE       :   str         =      val

    # finally, create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE       :   List[str]         =      DetrForSegmentation(A					) if is_panoptic else DetrForObjectDetection(A					)
							model.load_state_dict(A					)
							model.eval()

							# verify our conversion on an image
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
							SCREAMING_SNAKE_CASE       :   str         =      DetrImageProcessor(format=A					)

							SCREAMING_SNAKE_CASE       :   int         =      processor(images=prepare_img()       ,		return_tensors='''pt'''					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      encoding['''pixel_values''']

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      detr(A					)
							SCREAMING_SNAKE_CASE       :   int         =      model(A					)

							assert torch.allclose(outputs.logits       ,		original_outputs['''pred_logits''']       ,		atol=1e-3					)
							assert torch.allclose(outputs.pred_boxes       ,		original_outputs['''pred_boxes''']       ,		atol=1e-3					)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks       ,		original_outputs['''pred_masks''']       ,		atol=1e-4					)
							print('''Looks ok!'''					)

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
														Path(A					).mkdir(exist_ok=A					)
														model.save_pretrained(A					)
														processor.save_pretrained(A					)

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('''Uploading PyTorch model and image processor to the hub...'''					)
														model.push_to_hub(F"""nielsr/{model_name}"""					)
														processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Tuple             =    argparse.ArgumentParser()

							parser.add_argument(
							    '--model_name',
							    default='detr-resnet-50',
							    type=str,
							    choices=['detr-resnet-50', 'detr-resnet-101'],
							    help='Name of the DETR model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
							lowerCAmelCase_  :							int             =    parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import os

lowerCAmelCase_  :							Union[str, Any]             =    {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   str         =      0

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while index < len(A					) - 1:
														SCREAMING_SNAKE_CASE       :   Dict         =      SYMBOLS[numerals[index]]
														SCREAMING_SNAKE_CASE       :   str         =      SYMBOLS[numerals[index + 1]]
														if current_value < next_value:
																					total_value -= current_value
														else:
																					total_value += current_value
														index += 1
							total_value += SYMBOLS[numerals[index]]

							return total_value
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   Any         =      ''''''

							SCREAMING_SNAKE_CASE       :   Dict         =      num // 1000
							numerals += m_count * "M"
							num %= 1000

							SCREAMING_SNAKE_CASE       :   str         =      num // 100
							if c_count == 9:
														numerals += "CM"
														c_count -= 9
							elif c_count == 4:
														numerals += "CD"
														c_count -= 4
							if c_count >= 5:
														numerals += "D"
														c_count -= 5
							numerals += c_count * "C"
							num %= 100

							SCREAMING_SNAKE_CASE       :   Any         =      num // 10
							if x_count == 9:
														numerals += "XC"
														x_count -= 9
							elif x_count == 4:
														numerals += "XL"
														x_count -= 4
							if x_count >= 5:
														numerals += "L"
														x_count -= 5
							numerals += x_count * "X"
							num %= 10

							if num == 9:
														numerals += "IX"
														num -= 9
							elif num == 4:
														numerals += "IV"
														num -= 4
							if num >= 5:
														numerals += "V"
														num -= 5
							numerals += num * "I"

							return numerals
def        UpperCAmelCase (    A	:					str = "/p089_roman.txt"					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      0

							with open(os.path.dirname(A					) + roman_numerals_filename					) as filea:
														SCREAMING_SNAKE_CASE       :   str         =      filea.readlines()

							for line in lines:
														SCREAMING_SNAKE_CASE       :   Any         =      line.strip()
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parse_roman_numerals(A					)
														SCREAMING_SNAKE_CASE       :   Any         =      generate_roman_numerals(A					)
														savings += len(A					) - len(A					)

							return savings


if __name__ == "__main__":
							print(f'{solution() = }')




